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  • 2015-2019  (3)
  • Economics  (3)
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  • 2015-2019  (3)
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  • Economics  (3)
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  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2016
    In:  Mobile Information Systems Vol. 2016 ( 2016), p. 1-14
    In: Mobile Information Systems, Hindawi Limited, Vol. 2016 ( 2016), p. 1-14
    Abstract: Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first design improved k-means algorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2187808-0
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2019
    In:  Mobile Information Systems Vol. 2019 ( 2019-07-10), p. 1-11
    In: Mobile Information Systems, Hindawi Limited, Vol. 2019 ( 2019-07-10), p. 1-11
    Abstract: A massive amount of spatial-temporal records generated by sensors across the city help describe our day-to-day activities. Since the lifestyle represented by moving data varies from one individual to another, data analysts could facilitate the suspect-detection task by analyzing and classifying related trajectories of a given target. However, there are still some challenges that need to be overcome in real-life cases; for instance, the positive instances are limited, the trajectories are too diverse, and the transit behavior features are both too broader and costly to define. Moreover, people living in different areas of the city may have different life habits which can result in incorrect conclusions due to data-sensitive factors. In this paper, we describe the particular characteristics of movement behaviors regarding trajectory features. We also propose two models to improve the identification performance, namely, the trajectory pattern model (TPM) and neural network-based model. The trajectory pattern model (TPM) offers a novel view to describe users’ movement behaviors and generates more effective and universal features other than location and timestamp dimensions. The end-to-end neural network-based model aims to avoid picking human features. Statistical analysis and insightful explanations are provided to help understand the behavior of a given target. The effectiveness of our proposed solutions compared to peer solutions is demonstrated and proved via extensive evaluation.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2187808-0
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2017
    In:  Production and Operations Management Vol. 26, No. 3 ( 2017-03), p. 409-425
    In: Production and Operations Management, SAGE Publications, Vol. 26, No. 3 ( 2017-03), p. 409-425
    Abstract: A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.
    Type of Medium: Online Resource
    ISSN: 1059-1478 , 1937-5956
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
    detail.hit.zdb_id: 2151364-8
    detail.hit.zdb_id: 1108460-1
    SSG: 3,2
    Library Location Call Number Volume/Issue/Year Availability
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